Trevor Bedford Profile picture
14 Nov, 14 tweets, 5 min read
The #COVID19 epidemic is rapidly growing throughout the US. What happens now? Here I try to make some predictions, but mostly try to explain how I think about the epidemic. 1/14
The US just reported ~170,000 cases in a day. However, there hasn't been a sudden increase in transmission. This is the same exponential growth process going on for weeks now. Rt has ticked up from a US average of ~1 in August to ~1.15. Data from rt.live. 2/14
This increase in Rt can be ascribed to seasonality of the virus. Seasonality of respiratory pathogens is (incredibly) not well understood but is thought to be due to a combination of indoor crowding and increased stability of viral particles in drier winter air. 3/14
In the 2009 swine influenza pandemic we saw a spring wave, ongoing summer circulation and then a substantial and rapid fall wave when seasonality took effect in September. From cdc.gov/flu/weekly/wee…. 4/14
Similarly, the 1918 influenza pandemic showed summer circulation but had a rapid and substantial fall wave that resulted in most of the mortality of the pandemic. 5/14
In general, Rt depends on seasonality, societal behavior and population immunity. States that opened early had summer surges that resulted in perhaps 15% to 20% of the population infected before immunity and behavior brought surges under control. 6/14
Here I'm demonstrating this relationship in a contour plot that shows how social connectivity and population immunity influence Rt. In August, the US as a whole was roughly on the dashed line of Rt = 1. 7/14
Over the course of Sep and Oct, seasonality crept in and increased Rt by perhaps 20%. 8/14
This increase in Rt due to seasonality tilted the dynamic equilibrium towards transmission. Here, I'm showing the same plot of social connectivity and population immunity but with a 20% increase in R0 due to seasonality. 9/14
You can see that where we had been at Rt ~1 (shown as the black dot) now corresponds to Rt ~1.15 and results in an exponentially growing epidemic. 10/14
We now have a choice in terms of how we get back to Rt<1. The virus "wants" to take us to the right towards more infections, where population immunity brings transmission back down. Alternatively, we can scale back social connectivity to curb transmission. 11/14
A US-wide epidemic with Rt = 1.15 will result in an additional 25% of the population infected before the epidemic is resolved, while an epidemic with Rt = 1.2 will result in an additional ~30% of the population infected. 12/14
We're approaching these numbers in Wisconsin (310k total cases for ~1.2M infections in a population of 5.8M for ~21% of the state infected) and North Dakota (60k total cases for ~240k infections in a population of 760k for ~32% of the state infected). 13/14
If we don't take action soon the virus will decide for us. An eventual 30% of the US infected would correspond to ~450k deaths (at an IFR of 0.45%) and many more cases of #longcovid. 14/14

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More from @trvrb

13 Nov
A brief update on our work with the sequencing of the White House #COVID19 outbreak. Since posting on Nov 1, groups from all over the US have shared an additional 2798 #SARSCOV2 viral genomes via @gisaid and additional connections have emerged. 1/9
This sequencing has revealed additional viruses circulating in Virginia and collected between Aug and Oct that fall alongside the WH lineage, as well as three viruses from Michigan collected in Oct that are closely related to sequences from the White House outbreak. 2/9
These three viruses from Michigan possess 1 differentiating mutation and the two White House-associated viruses also possess 1 differentiating mutation. A molecular clock analysis places their common ancestor in Aug or Sep. Interactive figure at nextstrain.org/community/blab…. 3/9
Read 9 tweets
11 Nov
After posting about sharply rising #COVID19 cases Friday, there were multiple replies to the effect of "but deaths aren't going up". As should be obvious to most at this point, (reported) deaths lag (reported) cases. This thread investigates. 1/8
There is a lag between when a case is diagnosed and when the individual may succumb to their disease and there is a further lag between date of death and when the death is reported. 2/8
Here, I compare state-level data from @COVID19Tracking for cases and deaths and find that a 22-day lag maximizes state-level correlations. 3/8
Read 8 tweets
7 Nov
I know that everyone has been (justifiably) distracted by other things, but the #COVID19 epidemic in the US is looking pretty dire with 125,552 confirmed cases reported Friday by @COVID19Tracking. 1/10
Please consider this somewhat of a follow up to the thread two weeks ago on circulation patterns across states. 2/10
Confirmed cases have continued to tick up across the US, though with the Midwest and Mountain West contributing to most of the recent increase. Data from @COVID19Tracking. 3/10 Image
Read 10 tweets
1 Nov
Separately, I wanted to address the question of "why do this?" with regards to sequencing of infections involved in the White House #COVID19 outbreak. 1/10
Although the origins of the White House outbreak were characterized as "unknowable", viral genome sequencing can offer important clues to how infections in a cluster are connected to each other and to the larger COVID-19 epidemic. 2/10
This technology is rapidly becoming a standard course of action for COVID-19 clusters of public health interest. This seems obvious, but we can use science to understand and track the spread of COVID-19. 3/10
Read 10 tweets
1 Nov
Our research group at @fredhutch, @UWMedicine and @BrotmanBaty has sequenced the viral genomes of two SARS-CoV-2 infections that were connected to the White House #COVID19 outbreak. The @nytimes reports here: nytimes.com/2020/11/01/sci…. 1/16
We enrolled two individuals with exposures linked to the White House COVID-19 outbreak into an IRB-approved research study, collected nasal swabs and sequenced the SARS-CoV-2 virus in these swabs. 2/16
Importantly, these two individuals attested that they had no direct contact with each other in the days preceding their diagnoses and are independently linked to the White House COVID-19 outbreak. 3/16
Read 18 tweets
19 Oct
Daily #COVID19 case counts are increasing in the US and we seem to hitting a third wave (or second surge if you'd prefer). Here I wanted to look at how case counts through time correlate across different states. 1/12
I start with a simple coloring to group states in the West (in red), the Southwest (in orange), the Midwest (in green), the Southeast (in blue) and the Northeast (in purple). Color ramp borrowed from @andersonbrito_. 2/12
Using data from @COVID19Tracking, I plot daily confirmed cases for each state since March as a stacked chart. The three crests are obvious (though not clear how large the third will end up being). Different regions are contributing to each wave to different degrees. 3/12
Read 13 tweets

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